Challenge: Building socially-intelligent AI agents involves creating agents that can sense, perceive, reason about, learn from, and respond to affect, behavior, and cognition of other agents.
Approach: They propose a set of technical challenges and open questions for researchers to advance Social-AI.
Outcome: The proposed frameworks are based on the social intelligence competencies that evolved over thousands of years in Homo sapiens and are expected to be the foundations for the development of social-intelligent AI agents.

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Challenge: Large Language Models (LLMs) are a powerful tool for integrating human-like communication and context-aware interactions into artificial systems.
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Challenge: Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments.
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Challenge: Existing work on social intelligence in NLP does not provide a coherent subfield for researchers to analyze and identify research gaps and future directions.
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Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs (2022.emnlp-main)

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Challenge: Large language models are increasingly employed to empower autonomous agents to simulate human behavior.
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Research Community Perspectives on “Intelligence” and Large Language Models (2025.findings-acl)

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Connecting Language and Vision to Actions (P18-5)

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Challenge: The Science of Science (SciSc) examines how scientific knowledge is produced, evaluated, and transformed by utilizing large-scale scholarly and bibliometric data.
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